In this episode of the Computomics podcast, Dr. Rex Bernardo discusses the evolution of predictive plant breeding, from early molecular markers to today’s machine learning and AI approaches. He explains how these tools can support not only trait prediction, but also more complex breeding decisions, such as whether a line is likely to be selected or become a successful variety. The conversation also highlights community-focused work on leafy African vegetables in Minnesota and reflects on how plant breeding education must evolve to prepare future breeders for a more interdisciplinary, data-driven field.

In this episode, Darryn Keiller, Futurist, Entrepreneur, and founder of WayBeyond, discusses how digital agronomy helps growers make better decisions with data. He explains how farms can combine sensor, environmental, plant-response, and satellite data to improve daily decisions, predict risks, and raise yield and fruit quality. The conversation also covers the challenges of introducing new technology into agriculture, especially around trust and data ownership. Darryn argues that AI will not replace growers, but support them by closing knowledge gaps, and helping younger professionals adapt faster. It also enables farming systems to respond better to climate change, labor shortages, and growing production pressures.

More:

• Darryn Keiller on LinkedIn
• WayBeyond Homepage

Chris Kubica, Technical Account Manager at Computomics, discusses the importance of pangenomes for modern plant breeding. He explains that traditional genomics relies on a single reference genome, which cannot capture the full genetic diversity of a species. This is a big problem in complex plant genomes, where major parts of breeding-line diversity can be absent from the standard reference. A pangenome combines multiple assemblies to represent more of a species’ variation and to unlock hard-to-find trait signals. Chris introduces Pantograph, Computomics’ “omics browser”, which allows users to visualize and explore structural variation and integrate diverse omics data such as gene expression and variant information. Hear about current and planned capabilities in this informative and enjoyable episode!

More:

• Pantograph website
• Chris Kubica on LinkedIn

This episode of the Computomics Podcast features Vanessa Prigge, potato breeder and R[&]D project manager at Saka Pflanzenzucht (Solana Group). She discusses her international research background, the unique challenges of potato breeding - such as vegetative propagation, complex genetics, and long breeding cycles - and the need for compromise-driven selection. Vanessa highlights two major projects: IPMorama, which promotes integrated pest management through resistant varieties, monitoring, and stakeholder collaboration, and POMORROW, which unlocks potato gene bank diversity for future-proof breeding. Together, these efforts aim to make potato breeding more sustainable, collaborative, and future-ready..

More information:

• Solana Main Page
• IPMorama Project Website
• POMORROW project (German language)
• Vanessa on LinkedIn
  

In this episode of the Computomics podcast, Vonnie Estes, VP of Innovation at the International Fresh Produce Association (IFPA), shares insights on the challenges the produce industry faces, particularly around climate change, pest management, and cold chain logistics. She discusses the importance of AI and machine learning in enhancing supply chain efficiency and food safety. Vonnie also highlights IFPA’s initiatives, such as "Supply Chain of the Future" and efforts to reduce single-use plastic in packaging. A grant program supports innovation in the produce sector, offering funding opportunities for startups in developing solutions to industry problems.

More:

• Vonnie Estes on LinkedIn
• International Fresh Produce Organization (IFPA)
• The Fresh Supply Chain of the Future
• Fresh Takes on Tech podcast

In this episode of the Computomics podcast, Pernille Ollendorff Hede, CEO and co-founder of Healthycrop, discusses the company's innovative approach to fungal-resistant crops. Healthycrop boosts the production of Nepenthesin, a naturally occurring enzyme in plants, to degrade fungal enzymes, preventing fungal growth without the need for fungicides. Using cis-genesis and gene editing techniques, Healthycrop enhances the plant's natural defense. While these techniques are classified as genetically modified (GM) in Europe, they are considered non-GM in the U.S. Pernille shares the company's mission to revolutionize agriculture by offering sustainable solutions and hopes for regulatory changes in Europe to facilitate broader adoption.

More:

• Healthycrop - Homepage
• Pernille Ollendorff Hede on LinkedIn

In this episode of the Computomics podcast, bioinformatics analyst Michelle Hagen describes how her work bridges biology and data science. As a data analyst in bioinformatics and plant breeding, Michelle transforms raw genetic and environmental data into insights that guide breeding decisions. She particularly enjoys the teamwork, problem-solving, and creativity involved in her job.

Michelle works on Computomics’ Climate-Smart Breeding technology xSeedScore and the Metagenomics Analysis Platform MORPHEUS. Her main tasks involve performing data quality control, running bioinformatics pipelines, and creating interactive dashboards for customers. Her work supports the development of climate-resilient, high-yield crops, and it helps breeders make data-driven breeding faster and more precise.

Glendon Ascough, Research Director at Starke Ayres, discusses the evolution of the company from a historic seed merchant to Africa’s largest independent vegetable breeding company. He shares his personal journey into plant science, driven by a fascination with genetics, and highlights the company’s focus on breeding disease-resistant vegetable varieties tailored to local and international markets. He talks about the collaboration with Computomics, using machine learning to predict sweet corn hybrid performance. Glendon emphasizes the importance of high-quality, diverse phenotypic data and the shift in breeder mindset required for data-driven approaches.